Abstract
We present a tree-structured architecture for supervised learning. The statistical model underlying the architecture is a hierarchical mixture model in which both the mixture coefficients and the mixture components are generalized linear models (GLIM's). Learning is treated as a maximum likelihood problem; in particular, we present an Expectation-Maximization (EM) algorithm for adjusting the parameters of the architecture. We also develop an on-line learning algorithm in which the parameters are updated incrementally. Comparative simulation results are presented in the robot dynamics domain.
Keywords
Expectation–maximization algorithmAlgorithmMixture modelComputer scienceArtificial intelligenceLine (geometry)Tree (set theory)ArchitectureDomain (mathematical analysis)MathematicsMaximum likelihoodMachine learningPattern recognition (psychology)Statistics
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Publication Info
- Year
- 1994
- Type
- article
- Volume
- 6
- Issue
- 2
- Pages
- 181-214
- Citations
- 2555
- Access
- Closed
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Cite This
Michael I. Jordan,
Robert A. Jacobs
(1994).
Hierarchical Mixtures of Experts and the EM Algorithm.
Neural Computation
, 6
(2)
, 181-214.
https://doi.org/10.1162/neco.1994.6.2.181
Identifiers
- DOI
- 10.1162/neco.1994.6.2.181